The statistical discovery workflow of JMP is well-suited to situations when it’s not clear what’s in your data and how it can (or can’t) help meet your objectives. You can also import data directly from HTML tables embedded in web pages, or use Query Builder to streamline access to relational data. The import wizard in JMP supports many file types, including SPSS Desktop Reporter.
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Text Explorer extracts potentially useful patterns from a body of documents, each of which may be simple (for example, a free text response to a survey question) or complex (for example, comments from a technician undertaking scheduled maintenance of a piece of equipment).Īll of these analyses can be used with secondary data, too, but this can be harder to work with effectively since it was collected to meet different objectives.
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JMP Pro provides exact and asymptotic tests, giving a more reliable indication as to whether or not an association is real. Item Analysis provides an alternative approach when you have a set of items assigned yes/no values by respondents. If you have sparse data, defining “super categories” combines levels to increase cell counts, making results more reliable. You can explore and analyze the pattern of categorical responses from surveys, handling data in a variety of formats. You can interactively define tables of summary statistics with grouping variables and generate a multitude of graph types, including geographic maps. JMP provides many options for handling primary data, including Tabulate, Graph Builder, and Categorical platforms. Often, primary data is scarce and secondary data is abundant. Exploratory research is also used to formulate hypotheses and develop measurement scales.
Ill-defined problems and opportunities relating to consumers are usually clarified and refined using a combination of interviews, focus groups and observational and ethnographic studies. No matter what your level of statistical expertise, JMP helps you understand your data, find new consumer insights more quickly, and communicate these insights to other stakeholders to drive consensus and action. The agility and interactivity of JMP makes it particularly suited to ad hoc studies, but its programmability also provides options for expediting routine, ongoing studies, within the confines of a desktop application. Interdependence techniques treat all variables on an equal footing, whereas dependence techniques aim to identify relationships between groups of variables, usually between inputs (X’s) and outputs, outcomes or items (Y’s).
Whether you are conducting exploratory, descriptive or causal research using primary or secondary sources, JMP provides a comprehensive repertoire of tools for quickly and easily getting value from metric and non-metric quantitative data. Although the digital revolution offers the promise to positively change the dynamic with consumers, this opportunity will be realized only if you can successfully leverage new data to better understand what specific groups of consumers really want and how you can best meet, or even shape, their needs. Data is ubiquitous in all aspects of finding consumers and making them happy, from introducing new products or services, to positioning, branding, advertising, segmentation and promotion. In addition to a constant focus on the customer – current or potential – one of the imperatives is to be data-driven. Yet core business issues endure: finding the most profitable growth opportunities, developing the best products and services, taking the best marketing action, and maximizing cross-business impact.
Marketing is complex and driven by rapidly evolving digital technologies.
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